Inverse weighted clustering algorithm

Abstract

We discuss a new form of clustering which overcomes some of the problems of traditional K-means such as sensitivity to initial conditions. We illustrate convergence of the algorithm on a number of artificial data sets. We then introduce a variant of this clustering which preserves some aspects of global topology in the organisation of the centres. We illustrate on artificial data before using it to visualise some standard datasets.